Present day wireless systems provide support for a variety of high speed data applications. In order to achieve user acceptance and satisfaction, a system providing support for these applications must provide an adequate quality of service experience to users at an affordable price. With wireless bandwidth at a premium, traffic engineering and network planning play a vital role in meeting these needs. The use of Internet Protocol (IP) packets, one of the most widely used modes of data communication in wired networks, benefits greatly from the use of scheduling protocols and strategies that help to overcome the problems of scheduling packet transmissions under the widely varying channel conditions typical of most wireless environments. Channel aware and traffic aware scheduling that exploits the delay tolerance of data is a key source of performance enhancement for wireless data networks.
Special characteristics of data networks include higher layers of flow control, the presence of data users, who have more diverse and less predictable traffic behavior than voice users, and who tend to transmit data in bursts, rather than in a continuous stream and the need to support various types of quality of service requirements. These factors tend to make the control and prediction of data performance significantly more complex in data networks. Hence, optimal planning of data networks is considerably more complex than planning of conventional voice networks, and requires particular attention in order to optimize relevant factors.
Planning of wireless data networks requires various computations and considerations to be made in order to design, orient and place various elements of the network. Planning includes optimizing relevant characteristics of the network by adjusting various parameters of base stations employed in the network in order to maximize the coverage and capacity of the network, subject to requirements for the quality of service that must be provided to the users. Parameters to be adjusted include antenna azimuth and down tilt angles, and sector power levels.
In determining the design of a wireless network, it is important to understand the data transmission techniques used by the network, and their effect on the performance of the network as experienced by the users. As each user of a wireless network moves from one location to another, and as other events take place, such as increases and decreases in the number of users to be served, the user may experience changes in channel conditions. In addition, different users may experience differing channel conditions. For example, one user may be in a favorable location and may therefore experience a favorable channel condition, while another user may be in a less favorable location and may experience a less favorable channel condition. In addition, data users exhibit a considerable degree of delay tolerance. A data user typically does not need to receive data in a continuous stream, and may not notice delays in data transmission if the delays are not too severe. Data networks typically employ various scheduling strategies that take advantage of this delay tolerance on the part of individual users in order to achieve greater overall throughput. The transmission rate to a user depends in part on the quality of the channel experienced by the user. In typical scheduling strategies commonly used in wireless networks, a base station transmits more frequently to a user experiencing a favorable channel condition, and transmits less frequently to a user experiencing an unfavorable channel condition.
One commonly used category of scheduling techniques is proportional fair scheduling. During the use of this technique, the throughput of no single user can be improved without reducing the throughputs of the other users by a greater total percentage. This approach is referred to as proportional fairness. User experiences in a network employing a proportional fair scheduling technique can be described using various parameters, such as gain, throughput, blocking probability, and so on. Planning of a network preferably takes into account the specific scheduling strategies to be employed in the network.
One advantageous technique for planning a wireless network involves creating a model yielding various parameters showing the quality of service experienced by the user and establishing the placement and other relevant properties, such as antenna power and orientation, so that the quality of service experienced by the user, as indicated by the parameters generated by the model, meets predetermined requirements. Optimization may occur in an iterative fashion, in which properties of the network elements are repeatedly adjusted and the model parameters recalculated until an optimal set of parameters is achieved. It is theoretically possible to determine values of parameters indicating user level quality of service by performing a simulation of the network operation, obtaining the parameters yielded by the simulation, adjusting the network element properties and repeating the process until optimal results are achieved. However, particularly in cases of complex systems such as wireless data systems, simulation requires significant processing resources and optimization through simulation may present a significant computational burden, especially in cases in which optimization frequently follows an iterative process such as that described above.
There exists, therefore, a need for a system for wireless network planning that generates parameters needed for placement of network elements, including orientation and power of antennas, using techniques that provide rapid execution while maintaining a high degree of accuracy.